SGER: High-throughput Determination of Position Weight Matrices

转录因子通过以序列特异性方式结合双链DNA并调节新转录物的起始来调节基因表达。 转录因子独特的结合特异性是其调控基因表达能力的关键组成部分。 然而，确定结合特异性是耗时的，并且即使在充分研究的生物体中，也只有一小部分转录因子的详细信息可用。 该项目的目标是开发和验证一种快速，简单，高通量的检测方法，以确定任何转录因子的结合特异性（或位置权重矩阵）的全谱。 该方法使用含有数千种不同dsDNA发夹探针的微阵列来测定7个碱基对的所有排列的相对结合。 该方法将通过将结果与使用现有的更费力的方法获得的结果进行比较来进行测试，然后进行改进，校准和优化以供一般使用。 这种高通量的方法应该适用于了解几乎任何蛋白质的结合特异性：DNA相互作用。 预计这种方法将提供几个明显的优势，现有的方法确定位置权重矩阵：速度，简单，全面的采样所有潜在的结合位点。 该方法的成功验证将通过提供关于许多不同转录因子的结合特异性的详细信息而有益于基因表达机制的基础研究。基础研究的其他方面也将受益，包括基因组序列的自动注释和基因网络的进化分析。

Transcription factors regulate gene expression by binding in a sequence-specific manner to double-stranded DNA and modulating the initiation of new transcripts. The unique binding specificities of transcription factors are critical components of their ability to regulate gene expression. Yet determining binding specificity is time-consuming and detailed information is available for only a fraction of transcription factors, even in well-studied organisms. The goal of this project is to develop and validate a rapid, simple, high-throughput assay to determine the full spectrum of binding specificities (or position weight matrix) for any transcription factor. The approach uses a microarray containing thousands of different dsDNA hairpin probes to assay relative binding of all permutations of seven base pairs. This method will be tested by comparing results to those obtained using existing, more laborious methods, and then refined, calibrated, and optimized for general use. This high-throughput method should be applicable to understanding the binding specificity of almost any protein:DNA interaction. It is expected that this approach will offer several distinct advantages over existing methods for determining position weight matrices: speed, simplicity, and comprehensive sampling of all potential binding sites. Successful validation of this method should benefit basic research on mechanisms of gene expression by providing detailed information about the binding specificities of many different transcription factors. Other aspects of basic research will also benefit, including automated annotation of genome sequences and evolutionary analyses of gene networks.